code: def create_loader(
      dataset,
      input_size,
      batch_size,
      is_training=False,
      use_prefetcher=True,
      interpolation="bilinear",
      mean=IMAGENET_DEFAULT_MEAN,
      std=IMAGENET_DEFAULT_STD,
      num_workers=1,
      crop_pct=None,
      tensorflow_preprocessing=False,
  ):
      if isinstance(input_size, tuple):
          img_size = input_size[-2:]
      else:
          img_size = input_size
  
      if tensorflow_preprocessing and use_prefetcher:
          from data.tf_preprocessing import TfPreprocessTransform
  
          transform = TfPreprocessTransform(
              is_training=is_training,
              size=img_size,
              interpolation=interpolation,
          )
      else:
          transform = transforms_imagenet_eval(
              img_size,
              interpolation=interpolation,
              use_prefetcher=use_prefetcher,
              mean=mean,
              std=std,
              crop_pct=crop_pct,
          )
  
      dataset.transform = transform
  
      loader = torch.utils.data.DataLoader(
          dataset,
          batch_size=batch_size,
          shuffle=False,
          num_workers=num_workers,
          collate_fn=fast_collate
          if use_prefetcher
          else torch.utils.data.dataloader.default_collate,
      )
      if use_prefetcher:
          loader = PrefetchLoader(loader, mean=mean, std=std)
  
      return loader
  
file_path: gen-efficientnet-pytorch/data/loader.py
imports: import torch
  import torch.utils.data
  from .transforms import *
label_desc: Use image node_id 2 for calling a global function with example usage: create_loader(dataset, input_size, batch_size, is_training, use_prefetcher, interpolation, mean, std, num_workers, crop_pct, tensorflow_preprocessing) and returns: loader